基于卷积神经网络的轿车车型精细识别方法  

Fine-grained vehicle type recognition based on deep convolution neural networks

作　　者：陈宏彩[1,2] 程煜[1,2] 张常有[3] 

机构地区：[1]河北省科学院应用数学研究所,河北石家庄050081 [2]河北省信息安全认证工程技术研究中心,河北石家庄050081 [3]中国科学院软件研究所并行软件与计算科学实验室,北京100190

出　　处：《河北科技大学学报》2017年第6期564-569,共6页Journal of Hebei University of Science and Technology

基　　金：国家自然科学基金(61379048;61672508);河北省重点研发计划项目(17395602D);河北省三三三人才工程项目(2016022577-7)

摘　　要：在复杂交通场景中,公安和交管部门对车型识别的实时性和精度提出了更高要求。针对当前假牌、套牌、无牌车辆处理占用大量警力、检索效率低下、非智能化等一系列问题,提出了一种基于GoogleNet深度卷积神经网络的车型精细识别方法,设计了合理的卷积神经网络滤波器大小和数目,优选了激活函数和车型识别分类器,构建了一个新的卷积神经网络轿车车型精细识别模型框架。实验结果表明,在车型精细识别测试中,所提出模型的识别率达到了97%,较原始GoogleNet模型有较大提升,而且,新模型有效地减少了训练参数的数量,降低了模型的存储空间。车型精细识别技术可应用于智能交通管理领域,具有重要的理论研究价值与实践意义。Public security and traffic department put forward higher requirements for real-time performance and accuracy of vehicle type recognition in complex traffic scenes.Aiming at the problems of great plice forces occupation,low retrieval efficiency,and lacking of intelligence for dealing with false license,fake plate vehicles and vehicles without plates,this paper proposes a vehicle type fine-grained recognition method based GoogleNet deep convolution neural networks.The filter size and numbers of convolution neural network are designed,the activation function and vehicle type classifier are optimally selected,and a new network framework is constructed for vehicle type fine-grained recognition.The experimental results show that the proposed method has 97% accuracy for vehicle type fine-grained recognition and has greater improvement than the original GoogleNet model.Moreover,the new model effectively reduces the number of training parameters,and saves computer memory.Fine-grained vehicle type recognition can be used in intelligent traffic management area,and has important theoretical researchvalue and practical significance.

关 键 词：计算机神经网络 车型识别 卷积神经网络 精细识别 深度学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]